{"id":"W6992857030","doi":"","title":"Modelling Forest Inventory and Biophysical Variables for an Uneven-Aged Forest Using Multi-Source Remotely-Sensed Data","year":2018,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Categorical variable; Context (archaeology); Mode (computer interface); Data set; Measure (data warehouse)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007944627,0.0004470447,0.000297886,0.0009504493,0.0002203068,0.0007394852,0.0007892719,0.0004887916,0.0005926944],"category_scores_gemma":[0.001443717,0.0003290291,0.0005980603,0.001142116,0.0002659608,0.001024329,0.00032323,0.0004185464,0.0001798557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001198542,"about_ca_system_score_gemma":0.0008011979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03241319,"about_ca_topic_score_gemma":0.07162028,"domain_scores_codex":[0.9997945,0.00004828478,0.00001358601,0.00007476097,0.000038956,0.00002991798],"domain_scores_gemma":[0.999501,0.0002670475,0.0001098698,0.00004182815,0.00005031004,0.00002998505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002666942,0.0000675918,0.04338191,0.0000314298,0.00005393808,0.0001096536,0.00004680023,0.9398451,0.0006840508,0.00088154,0.0001497016,0.0147217],"study_design_scores_gemma":[0.000002944039,0.00001350603,0.01401601,0.000006097242,0.0000117092,0.00002767871,0.00003833321,0.9848413,0.0001545705,0.0006020426,0.0002777661,0.000007935606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8636023,0.0004790673,0.1318243,0.0001514227,0.00002521273,0.0000982294,0.001774247,0.0002841243,0.001761161],"genre_scores_gemma":[0.9659959,0.0002559872,0.0318774,0.00001964352,0.00001229547,0.00007374702,0.0009440208,0.00002622304,0.0007946948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03241319,"threshold_uncertainty_score":0.06444901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03114241615063941,"score_gpt":0.2337097843008842,"score_spread":0.2025673681502448,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}